Mechanistic modelling in large case-control studies of lung cancer risk from smoking

Contribution of smoking, disease history, and survival to lung cancer disparities in Black individuals

BACKGROUND

Lung cancer is the leading cause of cancer deaths and disproportionately affects self-identified Black or African American ("Black") people, especially considering their relatively low self-reported smoking intensity rates. This study aimed to determine the relative impact of smoking history and lung cancer incidence risk, histology, stage, and survival on these disparities.

METHODS

We used 2 lung cancer models (MichiganLung-All Races and MichiganLung-Black) to understand why Black people have higher rates of lung cancer deaths. We studied how different factors, such as smoking behaviors, cancer development, histology, stage at diagnosis, and lung cancer survival, contribute to these differences.

RESULTS

Adjusted for smoking history, approximately 90% of the difference in lung cancer deaths between the overall and Black populations (born in 1960) was the result of differences in the risk of getting lung cancer. Differences in the histology and stage of lung cancer and survival had a small impact (4% to 6% for each). Similar results were observed for the 1950 and 1970 birth cohorts, regardless of their differences in smoking patterns from the 1960 cohort.

CONCLUSIONS

After taking smoking into account, the higher rate of lung cancer deaths in Black people can mostly be explained by differences in the risk of developing lung cancer. As lung cancer treatments and detection improve, however, other factors may become more important in determining differences in lung cancer mortality between the Black and overall populations. To prevent current disparities from becoming worse, it is important to make sure that these improvements are available to everyone in an equitable way.

Smoking and Lung Cancer Mortality in the United States From 2015 to 2065: A Comparative Modeling Approach

BACKGROUND

Tobacco control efforts implemented in the United States since the 1960s have led to considerable reductions in smoking and smoking-related diseases, including lung cancer.

OBJECTIVE

To project reductions in tobacco use and lung cancer mortality from 2015 to 2065 due to existing tobacco control efforts.

DESIGN

Comparative modeling approach using 4 simulation models of the natural history of lung cancer that explicitly relate temporal smoking patterns to lung cancer rates.

SETTING

U.S. population, 1964 to 2065.

PARTICIPANTS

Adults aged 30 to 84 years.

MEASUREMENTS

Models were developed using U.S. data on smoking (1964 to 2015) and lung cancer mortality (1969 to 2010). Each model projected lung cancer mortality by smoking status under the assumption that current decreases in smoking would continue into the future (status quo trends). Sensitivity analyses examined optimistic and pessimistic scenarios.

RESULTS

Under the assumption of continued decreases in smoking, age-adjusted lung cancer mortality was projected to decrease by 79% between 2015 and 2065. Concomitantly, and despite the expected growth, aging, and longer life expectancy of the U.S. population, the annual number of lung cancer deaths was projected to decrease from 135 000 to 50 000 (63% reduction). However, 4.4 million deaths from lung cancer are still projected to occur in the United States from 2015 to 2065, with about 20 million adults aged 30 to 84 years continuing to smoke in 2065.

LIMITATION

Projections assumed no changes to tobacco control efforts in the future and did not explicitly consider the potential effect of lung cancer screening.

CONCLUSION

Tobacco control efforts implemented since the 1960s will continue to reduce lung cancer rates well into the next half-century. Additional prevention and cessation efforts will be required to sustain and expand these gains to further reduce the lung cancer burden in the United States.

PRIMARY FUNDING SOURCE

National Cancer Institute.

Similarity of Slovak Regions in Neoplastic Mortality in the Context of Risk Factors and Access to Health Care

AIM

Access to primary health care is highly connected to the prevention of cancer mortality, since the risk factors threatening health can be early identified. The aim of this paper is, firstly, to explore similarity within and between the regions of the Slovak Republic and cancer mortality patterns, and secondly, to reveal if similar regions are characterised by the similar access to health care or risk factors occurrence.

METHODS

Data on deaths by sex, type of cancer death and region from 1996 to 2014 is provided by the National Health Information Centre of Slovakia. The relationships between 8 regions and 16 cancer types are described by correspondence analysis for both sexes.

RESULTS

The most similar cancer mortality patterns among Slovak regions are between the Nitra and Trnava regions for both sexes, and the Košice region for males. The Prešov region is showed as an outlier from other regions for females, likely due to the highest concentration of Roma marginalised communities. As for access to health care, the Trnava region as well as Nitra region report the lowest densities of physicians, 2.4 and 2.6 per 1,000 inhabitants, respectively. The most serious cancer types mortality is attributed to the digestive organs (C15-C26) in each Slovak region for both sexes with the average proportion of 35.56%. Observed high association between the Nitra region and respiratory cancer (C30-C39) in males may be confirmed by the increased incidence of radon in this region. Similarly, a tight relationship between the Bratislava region and cancer of male genital organs (C60-C63) can relate to the highest proportion of drug users in the Bratislava region.

CONCLUSIONS

Based on the findings of similar regions in cancer mortality patterns, we recommend to set the same prevention programs in the Trnava and Nitra regions, on the other hand, different preventive interventions should be introduced in the Prešov region.

Using the Negative Exponential Model to Describe Changes in Risk of Smoking-Related Diseases following Changes in Exposure to Tobacco

Recently published analyses for four smoking-related diseases show that the declining excess relative risk by time quit is well fitted by the negative exponential model. These analyses estimated the half-life of this excess, that is, the time after quitting when the excess relative risk reaches half that for continuing smokers. We describe extensions of the simple model. One quantifies the decline following an exposure reduction. We show that this extension satisfactorily predicts results from studies investigating the effect of reducing cigarette consumption. It may also be relevant to exposure reductions following product-switching. Another extension predicts changes in excess relative risk occurring following multiple exposure changes over time. Suitable published epidemiological data are unavailable to test this, and we recommend its validity to be investigated using large studies with data recorded on smoking habits at multiple time points in life. The basic formulae described assume that the excess relative risk for a continuing smoker is linearly related to exposure and that the half-life is invariant of age. We describe model adaptations to allow for nonlinear dose-response and for age-dependence of the half-life. The negative exponential model, though relatively simple, appears to have many potential uses in epidemiological research for summarizing variations in risk with exposure changes.

Dynamics of the risk of smoking-induced lung cancer: a compartmental hidden Markov model for longitudinal analysis

BACKGROUND

To account for the dynamic aspects of carcinogenesis, we propose a compartmental hidden Markov model in which each person is healthy, asymptomatically affected, diagnosed, or deceased. Our model is illustrated using the example of smoking-induced lung cancer.

METHODS

The model was fitted on a case-control study nested in the European Prospective Investigation into Cancer and Nutrition study, including 757 incident cases and 1524 matched controls. Estimation was done through a Markov Chain Monte Carlo algorithm, and simulations based on the posterior estimates of the parameters were used to provide measures of model fit. We performed sensitivity analyses to assess robustness of our findings.

RESULTS

After adjusting for its impact on exposure duration, age was not found to independently drive the risk of lung carcinogenesis, whereas age at starting smoking in ever-smokers and time since cessation in former smokers were found to be influential. Our data did not support an age-dependent time to diagnosis. The estimated time between onset of malignancy and clinical diagnosis ranged from 2 to 4 years. Our approach yielded good performance in reconstructing individual trajectories in both cases (sensitivity >90%) and controls (sensitivity >80%).

CONCLUSION

Our compartmental model enabled us to identify time-varying predictors of risk and provided us with insights into the dynamics of smoking-induced lung carcinogenesis. Its flexible and general formulation enables the future incorporation of disease states, as measured by intermediate markers, into the modeling of the natural history of cancer, suggesting a large range of applications in chronic disease epidemiology.

Impact of reduced tobacco smoking on lung cancer mortality in the United States during 1975-2000

BACKGROUND

Considerable effort has been expended on tobacco control strategies in the United States since the mid-1950s. However, we have little quantitative information on how changes in smoking behaviors have impacted lung cancer mortality. We quantified the cumulative impact of changes in smoking behaviors that started in the mid-1950s on lung cancer mortality in the United States over the period 1975-2000.

METHODS

A consortium of six groups of investigators used common inputs consisting of simulated cohort-wise smoking histories for the birth cohorts of 1890 through 1970 and independent models to estimate the number of US lung cancer deaths averted during 1975-2000 as a result of changes in smoking behavior that began in the mid-1950s. We also estimated the number of deaths that could have been averted had tobacco control been completely effective in eliminating smoking after the Surgeon General's first report on Smoking and Health in 1964.

RESULTS

Approximately 795,851 US lung cancer deaths were averted during the period 1975-2000: 552,574 among men and 243,277 among women. In the year 2000 alone, approximately 70,218 lung cancer deaths were averted: 44,135 among men and 26,083 among women. However, these numbers are estimated to represent approximately 32% of lung cancer deaths that could have potentially been averted during the period 1975-2000, 38% of the lung cancer deaths that could have been averted in 1991-2000, and 44% of lung cancer deaths that could have been averted in 2000.

CONCLUSIONS

Our results reflect the cumulative impact of changes in smoking behavior since the 1950s. Despite a large impact of changing smoking behaviors on lung cancer deaths, lung cancer remains a major public health problem. Continued efforts at tobacco control are critical to further reduce the burden of this disease.

A smoking-based carcinogenesis model for lung cancer risk prediction

Lung cancer is the leading cancer killer for both men and women worldwide. Over 80% of lung cancers are attributed to smoking. In this analysis, the authors propose to use a two-stage clonal expansion (TSCE) model to predict an individual's lung cancer risk based on gender and smoking history. The TSCE model is traditionally fitted to prospective cohort data. Here, the authors describe a new method that allows for the reconstruction of cohort data from the combination of risk factor data obtained from a case-control study, and tabled incidence/mortality rate data, and discuss alternative approaches. The method is applied to fit a TSCE model based on smoking. The fitted model is validated against independent data from the control arm of a lung cancer chemoprevention trial, CARET, where it accurately predicted the number of lung cancer deaths observed.

Modeling the mortality reduction due to computed tomography screening for lung cancer

BACKGROUND

The efficacy of computed tomography (CT) screening for lung cancer remains controversial because results from the National Lung Screening Trial are not yet available. In this study, the authors used data from a single-arm CT screening trial to estimate the mortality reduction using a modeling-based approach to construct a control comparison arm.

METHODS

To estimate the potential lung cancer mortality reduction because of CT screening, a previously developed and validated model was applied to the screening trial to predict the number of lung cancer deaths in the absence of screening. By using age, gender, and smoking characteristics matching those of the trial participants, the model was used to simulate 5000 trials in the absence of CT screening to produce the expected number of lung cancer deaths along with 95% confidence intervals (95% CIs), while adjusting for healthy volunteer bias.

RESULTS

There were 64 observed lung cancer deaths in the screening cohort (n = 7995), whereas the model predicted 117.7 deaths (95% CI, 98 deaths-139 deaths), indicating a mortality reduction of 45.6% (P < .001). When a more conservative healthy volunteer adjustment was applied, 111.3 lung cancer deaths were predicted (95% CI, 91 deaths-132 deaths), for a lung cancer-specific mortality reduction of 42.5% (P < .001).

CONCLUSIONS

The results of the current study indicate that CT screening along with early stage treatment can reduce lung cancer-specific mortality. This mortality reduction is greatly influenced by the protocol of nodule follow-up and treatment, and the length of follow-up.

Estimation of the effects of smoking and DNA repair capacity on coefficients of a carcinogenesis model for lung cancer

Numerous prospective and retrospective studies have clearly demonstrated a dose-related increased lung cancer risk associated with cigarette smoking, with evidence also for a genetic component to risk. In this study, using the two-stage clonal expansion stochastic model framework, for the first time we investigated the roles of both genetic susceptibility and smoking history in the initiation, clonal expansion, and malignant transformation processes in lung carcinogenesis, integrating information collected by a case-control study and a large-scale prospective cohort study. Our results show that individuals with suboptimal DNA repair capacity have enhanced transition rates of key events in carcinogenesis.

Analysis of lung cancer incidence in the Nurses' Health and the Health Professionals' Follow-Up Studies using a multistage carcinogenesis model

We analyzed lung cancer incidence among non-smokers, continuing smokers, and ex-smokers in the Nurses Health Study (NHS) and the Health Professionals Follow-Up Study (HPFS) using the two-stage clonal expansion (TSCE) model. Age-specific lung cancer incidence rates among non-smokers are identical in the two cohorts. Within the framework of the model, the main effect of cigarette smoke is on the promotion of partially altered cells on the pathway to cancer. Smoking-related promotion is somewhat higher among women, whereas smoking-related malignant conversion is somewhat lower. In both cohorts the relative risk for a given daily level of smoking is strongly modified by duration. Among smokers, the incidence in NHS relative to that in HPFS depends both on smoking intensity and duration. The age-adjusted risk is somewhat larger in NHS, but not significantly so. After smokers quit, the risk decreases over a period of many years and the temporal pattern of the decline is similar to that reported in other recent studies. Among ex-smokers, the incidence in NHS relative to that in HPFS depends both on previous levels of smoking and on time since quitting. The age-adjusted risk among ex-smokers is somewhat higher in NHS, possibly due to differences in the age-distribution between the two cohorts.

Lung cancer risk in mice: analysis of fractionation effects and neutron RBE with a biologically motivated model

Data from Argonne National Laboratory on lung cancer in 15,975 mice with acute and fractionated exposures to gamma rays and neutrons are analyzed with a biologically motivated model with two rate-limiting steps and clonal expansion. Fractionation effects and effects of radiation quality can be explained well by the estimated kinetic parameters. Both an initiating and a promoting action of neutrons and gamma rays are suggested. While for gamma rays the initiating event is described well with a linear dose-rate dependence, for neutrons a nonlinear term is needed, with less effectiveness at higher dose rates. For the initiating event, the neutron RBE compared to gamma rays is about 10 when the dose rate during each fraction is low. For higher dose rates this RBE decreases strongly. The estimated lifetime relative risk for radiation-induced lung cancers from 1 Gy of acute gamma-ray exposure at an age of 110 days is 1.27 for male mice and 1.53 for female mice. For doses less than 1 Gy, the effectiveness of fractionated exposure to gamma rays compared to acute exposure is between 0.4 and 0.7 in both sexes. For lifetime relative risk, the RBE from acute neutrons at low doses is estimated at about 10 relative to acute gamma-ray exposure. It decreases strongly with dose. For fractionated neutrons, it is lower, down to about 4 for male mice.

Analysis of epidemiological cohort data on smoking effects and lung cancer with a multi-stage cancer model

A stochastic two-stage cancer model is used to analyse the relation between lung cancer and cigarette smoking. The model contains the main rate-limiting stages of carcinogenesis, which include initiation, promotion (clonal expansion of initiated cells), malignant transformation and a lag time for tumour formation. Various data sets were used to test the model. These include the data of a large prospective collaborative project carried out in 10 different European countries, the European Prospective Investigation into Cancer and Nutrition (EPIC). This new data set has not been modelled before. The model is also tested on other published data from CPS-II (Cancer Prevention Study II) of the American Cancer Society and the British doctors' study. The analyses indicate that the EPIC data are best described with smoking dependence on the rates of malignant transformation and clonal expansion. With increasing smoking rates, saturation effects in the two exposure rate-dependent model parameters were observed. The results find confirmation in the biological literature, where both mutational effects and promotional effects of cigarette smoke are documented.

Interaction of smoking and radon in rats: a biologically based mechanistic model

Data on rats exposed to cigarette smoke before or after exposure to radon are used to estimate smoke-dependent parameters of the biologically based two-stage clonal expansion model. The baseline parameters and the action of radon acting on initiation and promotion were fixed based on earlier work. Cigarette smoke acting on transformation and inducing a reduction of the radon dose to the target cells after a smoking period gives an acceptable description of the data.

Multistage carcinogenesis and lung cancer mortality in three cohorts

Experimental evidence indicates that tobacco smoke acts both as an initiator and a promoter in lung carcinogenesis. We used the two-stage clonal expansion model incorporating the ideas of initiation, promotion, and malignant conversion to analyze lung cancer mortality in three large cohorts, the British Doctors' cohort and the two American Cancer Society cohorts, to determine how smoking habits influence age-specific lung cancer rates via these mechanisms. Likelihood ratio tests indicate that smoking-related promotion is the dominant model mechanism associated with lung cancer mortality in all cohorts. Smoking-related initiation is less important than promotion but interacts synergistically with it. Although no information on ex-smokers is available in these data, the model with estimated variables can be used to project risks among ex-smokers. These projected risks are in good agreement with the risk among ex-smokers derived from other studies. We present 10-year projected risks for current and former smokers adjusted for competing causes of mortality. The importance of smoking duration on lung cancer risk in these cohorts is a direct consequence of promotion. Intervention and treatment strategies should focus on promotion as the primary etiologic mechanism in lung carcinogenesis.

Studies of radon-exposed miner cohorts using a biologically based model: comparison of current Czech and French data with historic data from China and Colorado

The biologically based two-stage clonal expansion (TSCE) model is used to analyze lung cancer in several miners studies, two new ones (Czech, French) and two historic ones (Chinese, Colorado). In all cases, the model assumptions are identical. An action of radiation on initiation, promotion, and transformation is allowed. While all four studies indicate a highly significant action of radiation on promotion, the action on initiation is not significant in the French cohort, and barely significant in the Colorado miners cohort. No action on transformation is found in the Colorado miners, while the other data sets indicate a borderline significance. The model can describe all the data sets adequately, with different model parameters. The observed patterns in exposure, time since beginning of exposure, birth year, age and calendar year are reproduced well. The doubling exposure rate for initiation is about 3.5 WLM/year in the new data sets, while it is higher in the historic data sets. For transformation the doubling rate is about 20 WLM/year for the new data sets, while again the historic data give higher estimates. The action of radiation on promotion is quite different in the four data sets. These differences also induce different risk estimates at low exposures. The larger power of the new studies at these low exposures, compared to the historic data requires less extrapolation when the risk at very low exposures is estimated.

Comparing regression methods for the two-stage clonal expansion model of carcinogenesis

In the statistical analysis of cohort data with risk estimation models, both Poisson and individual likelihood regressions are widely used methods of parameter estimation. In this paper, their performance has been tested with the biologically motivated two-stage clonal expansion (TSCE) model of carcinogenesis. To exclude inevitable uncertainties of existing data, cohorts with simple individual exposure history have been created by Monte Carlo simulation. To generate some similar properties of atomic bomb survivors and radon-exposed mine workers, both acute and protracted exposure patterns have been generated. Then the capacity of the two regression methods has been compared to retrieve a priori known model parameters from the simulated cohort data. For simple models with smooth hazard functions, the parameter estimates from both methods come close to their true values. However, for models with strongly discontinuous functions which are generated by the cell mutation process of transformation, the Poisson regression method fails to produce reliable estimates. This behaviour is explained by the construction of class averages during data stratification. Thereby, some indispensable information on the individual exposure history was destroyed. It could not be repaired by countermeasures such as the refinement of Poisson classes or a more adequate choice of Poisson groups. Although this choice might still exist we were unable to discover it. In contrast to this, the individual likelihood regression technique was found to work reliably for all considered versions of the TSCE model.

A biologically based model for liver cancer risk in the Swedish thorotrast patients

Data on liver tumors among 416 Swedish patients who were exposed to Thorotrast between 1930 and 1950 were analyzed with the biologically based two-step clonal expansion (TSCE) model. For background data, the Swedish Cancer Register for the follow-up period 1958 to 1997 was used. Effects of radiation on the initiating mutation and on the clonal expansion rate explained the observed patterns well. The TSCE model permits the deduction of several kinetic parameters of the postulated tumorigenesis process. Dose rates of 5 mGy/year double the spontaneous initiation rate. The clonal expansion rate is doubled by 80 mGy/year, and for females it reaches a plateau at dose rates beyond 240 mGy/year. For males the plateau is not significant. The magnitude of the estimated promoting effect of radiation can be explained with a moderate increase in the cell replacement probability for the intermediate cells in the liver, which is strikingly similar to the situation in lung tumorigenesis.